Call for Papers

Call for short papers (pdf)

This sub-theme explores how innovative methods impact and inform our work and research practices, particularly those of qualitatively oriented scholars interested in macrolevel phenomena. Various computational approaches use language to study phenomena at the macro level, such as shifting fields, polarization, and discursive struggles. This level of analysis comprises various social settings in which actors define and negotiate social and cultural meanings—settings characterized by discursive “language games” (à la Ludwig Wittgenstein) that transcend individual organizations. Actors construct meanings that shape what is considered legitimate knowledge, values, authority, and rules of the game (Phillips et al., 2004). These discourses sediment in texts, words, images, and gestures in various macrolevel settings, including industries and fields (Augustine & King, 2019; Bohn & Gümüsay, 2024), social movements (King & Nelson, 2023), online communities (Safadi et al., 2021), ecosystems (Hannigan et al., 2022), and public discourse (Poschmann, 2025). Thus, different social settings operate through their own semi-autonomous language games, whose rules are learned through the application of and response to shared (and negotiated) practices, not instruction. Examining language games across settings provides novel insights into meaning-making processes, including cultural lock-ins (Fligstein et al., 2017), the fluctuation of frames (Bail, 2012), or the shifting meaning of a dominant frame itself (Bohn et al., 2026).

Due to the complexity and scope of language games over long time periods, computational methods have long been considered valuable tools for investigating processes of meaning construction (Mohr, 1998). Consequently, researchers studying discursive phenomena have integrated computational methods into their repertoire to study field evolution and discourse over several decades (Bail, 2012; Croidieu & Kim, 2018). Some researchers have further developed novel methodological approaches using text analysis methods, such as topic modeling and word embeddings (Hannigan et al., 2019; Poschmann et al., 2024). Such approaches aim to make the use of computational methods transparent, valid, and reliable (Evans & Aceves, 2016; Mohr et al., 2015; Nelson, 2021). Others have focused on combining these new methods with qualitative approaches to leverage the long-standing strengths of interpretive approaches on a larger scale (Bohn & Gümüsay, 2024; Goldenstein et al., 2026; Nelson, 2020). Ongoing discussions address how LLMs and generative artificial intelligence may transform empirical methodologies and organizational research in general (Bechky & Davis, 2025; Kulkarni et al., 2024). We can observe early applications of LLM-based tools, such as ChatGPT, to support manual coding and even for fully automated coding in qualitative contexts (e.g., Chattopadhyay et al., 2025; Garcia Quevedo et al., 2025). However, we are still in the process of exploring the ontological and epistemological consequences for our research practices and, more broadly, how these methods can support research on macrolevel phenomena.

Given the variety of applications and discussions surrounding new computational methods, the idea of “language games” has multiple meanings. First, computational methods may enable researchers to expand their studies of discourses at the macro level, providing an additional layer for understanding organizational phenomena. In addition, they offer the opportunity to capture complex negotiation processes involving a variety of actors over several decades or potentially even centuries. Second, researchers themselves contribute to language games by developing new methodological approaches that influence academic discourse and shape practices regarding how knowledge is generated and which methods are considered legitimate. In this context, critical questions arise about the use of computational methods, especially LLMs, which do not provide the transparency that we expect as a research community. Furthermore, the labels attached to new technologies are themselves embedded in language games because artificial intelligence, machine learning, and deep learning are not purely technical descriptions, but also carry future imaginaries, controversial narratives, and interest-based claims. This raises questions about how the interplay of technical affordances, current applications, and imaginaries surrounding technology is changing practices not only in organizational contexts, but also in the academic world.

For our sub-theme, we invite empirical, methodological, and phenomenon-based papers that explore aspects at the intersection of computational methods, qualitative research, and macrolevel phenomena. Submissions to this sub-theme may focus on any of the following questions, but are not limited to them:

Computational methods for macrolevel research

• How can new computational methods be used to study the emergence, evolution, and dissolution of meanings in macro discourses?

• What possibilities exist for generative reasoning models, simulations, and counterfactuals in modeling macrolevel decisions or dynamics?

• How can innovative methods facilitate the study of relationships between actors at levels beyond the organization?

• In the digital age of abundant digital data, how can extensive textual material be used to trace the emergence and evolution of industries, fields, communities, and social movements?

Effects of computational methods on the rigor and validity of empirical studies

• How can we ensure transparency, validity, and reliability when using novel computational methods to study discursive phenomena?

• How can we open the black box of LLM-based methods in the context of macrolevel investigations?

• How can we combine computational methods with more classical qualitative approaches to study macro-discursive phenomena?

The language game(s) inherent in new methods and technologies

• What are the dominant imaginaries, narratives, claims, and frames within discussions about AI, deep learning, and other technologies and innovations?

• How can we probe how language models learn (or do not learn) meaning, and what insights can we gain from such probes about how organizations, fields, and institutions negotiate and convey meaning?

• How do different actors such as policymakers, big tech companies, entrepreneurs, social movements, researchers, or labor unions influence and shape the language games around AI, machine learning, or robotics?
   

• Augustine, G., & King, B. G. (2019). Worthy of debate: Discursive coherence and agreement in the formation of the field of sustainability in higher education. Socio-Economic Review, 17(1), 135–165. https://doi.org/10.1093/ser/mwz020
• Bail, C. A. (2012). The fringe effect: Civil society organizations and the evolution of media discourse about Islam since the September 11th attacks. American Sociological Review, 77(6), 855–879. https://doi.org/10.1177/0003122412465743
• Bechky, B. A., & Davis, G. F. (2025). Resisting the algorithmic management of science: Craft and community after generative AI. Administrative Science Quarterly, 70(1), 1–22. https://doi.org/10.1177/00018392241304403
• Bohn, S., Lohmeyer, N., Jha, H. K., & Reinecke, J. (2026). Co-opting the security frame in the nuclear energy debate. Academy of Management Journal. Advance online publication. https://doi.org/10.5465/amj.2024.0656
• Bohn, S., & Gümüsay, A. A. (2024). Growing institutional complexity and field transition: Towards constellation complexity in the german energy field. Journal of Management Studies, 61(7), 3184–3225. https://doi.org/10.1111/joms.13004
• Chattopadhyay, S., Honoré, F., & Won, S. (2025). Free range startups? Market scope, academic founders, and the role of general knowledge in AI. Strategic Management Journal, 46(4), 1027–1079. https://doi.org/10.1002/smj.3685
• Croidieu, G., & Kim, P. H. (2018). Labor of love: Amateurs and lay-expertise legitimation in the early U.S. radio field. Administrative Science Quarterly, 63(1), 1–42. https://doi.org/10.1177/0001839216686531
• Evans, J. A., & Aceves, P. (2016). Machine translation: Mining text for social theory. Annual Review of Sociology, 42(1), 21–50. https://doi.org/10.1146/annurev-soc-081715-074206
• Fligstein, N., Brundage, J. S., & Schultz, M. (2017). Seeing like the Fed: Culture, cognition, and framing in the failure to anticipate the financial crisis of 2008. American Sociological Review, 82(5), 879–909. https://doi.org/10.1177/0003122417728240
• Garcia Quevedo, D., Glaser, A., & Verzat, C. (2025). Enhancing theorization using artificial intelligence: Leveraging large language models for qualitative analysis of online data. Organizational Research Methods, 29(1), 92–112. https://doi.org/10.1177/10944281251339144
• Goldenstein, J., Jancsary, D., Grodal, S., Forgues, B., & Jennings, P. D. (2026). Studying culture and meaning through interpretative computational methods: From theory to method and back. Organization Studies, 47(1), 7–32. https://doi.org/10.1177/01708406251410383
• Hannigan, T. R., Briggs, A. R., Valadao, R., Seidel, M.-D. L., & Jennings, P. D. (2022). A new tool for policymakers: Mapping cultural possibilities in an emerging AI entrepreneurial ecosystem. Research Policy, 51(9), Article 104315. https://doi.org/10.1016/j.respol.2021.104315
• Hannigan, T. R., Haans, R. F. J., Vakili, K., Tchalian, H., Glaser, V. L., Wang, M. S., Kaplan, S., & Jennings, P. D. (2019). Topic modeling in management research: Rendering new theory from textual data. Academy of Management Annals, 13(2), 586–632. https://doi.org/10.5465/annals.2017.0099
• King, B. G., & Nelson, L. K. (2023). Beyond protests: Using computational text analysis to explore a greater variety of social movement activities. In T. V. Maher & E. W. Schoon (Eds.), Methodological advances in research on social movements, conflict, and change (pp. 13–35). Emerald Publishing Limited. https://doi.org/10.1108/S0163-786X20230000047002
• Kulkarni, M., Mantere, S., Vaara, E., van den Broek, E., Pachidi, S., Glaser, V. L., Gehman, J., Petriglieri, G., Lindebaum, D., Cameron, L. D., Rahman, H. A., Islam, G., & Greenwood, M. (2024). The future of research in an artificial intelligence-driven world. Journal of Management Inquiry, 33(3), 207–229. https://doi.org/10.1177/10564926231219622
• Mohr, J. W. (1998). Measuring meaning structures. Annual Review of Sociology, 24(1), 345–370. https://doi.org/10.1146/annurev.soc.24.1.345
• Mohr, J. W., Wagner-Pacifici, R., & Breiger, R. L. (2015). Toward a computational hermeneutics. Big Data & Society, 2(2), 1–8. https://doi.org/10.1177/2053951715613809
• Nelson, L. K. (2020). Computational grounded theory: A methodological framework. Sociological Methods & Research, 49(1), 3–42. https://doi.org/10.1177/0049124117729703
• Nelson, L. K. (2021). Leveraging the alignment between machine learning and intersectionality: Using word embeddings to measure intersectional experiences of the nineteenth century U.S. South. Poetics, 88, Article 101539. https://doi.org/10.1016/j.poetic.2021.101539
• Phillips, N., Lawrence, T. B., & Hardy, C. (2004). Discourse and institutions. Academy of Management Review, 29(4), 635–652. https://doi.org/10.5465/amr.2004.14497617
• Poschmann, P. (2025). Capturing institutional logics and the construction of digital technologies: A macro-discursive perspective using formal text analysis methods. Information and Organization, 35(4), Article 100598. https://doi.org/10.1016/j.infoandorg.2025.100598
• Poschmann, P., Goldenstein, J., Büchel, S., & Hahn, U. (2024). A vector space approach for measuring relationality and multidimensionality of meaning in large text collections. Organizational Research Methods, 27(4), 650–680. https://doi.org/10.1177/10944281231213068
• Safadi, H., Johnson, S. L., & Faraj, S. (2021). Who contributes knowledge? Core-periphery tension in online innovation communities. Organization Science, 32(3), 752–775. https://doi.org/10.1287/orsc.2020.1364